Pattern recognition is a method of identification based on recognizing patterns of features in digital images. For example, iris recognition uses an image of the irises of an individual's eyes to identify a person and fingerprint recognition uses an image of friction ridge skin impressions to identify a person. As another example, a spacecraft identifies its orientation in space through recognition of star tracker images of star patterns. Obtaining a desired orientation in space permits a communication satellite to maintain a solar array pointed at the sun for electrical power generation and communication antennas pointed at the earth for radiofrequency communications.
Problems that may be encountered in pattern recognition include ambiguity, false patterns, misidentification, and complexity due to measurement errors and field-of-view limitations. As an example, friction ridge distortion in a fingerprint due to excessive pressing can introduce false patterns. Also, smudges in a fingerprint can introduce measurement errors. As another example, changes in pupil size due to changes in light can introduce false patterns in an iris. Measurement errors can occur in iris measurement if a person to be identified does not hold still and/or look directly into a video camera.
As a further example, a star pattern may be ambiguous if it lies within the measurement error radius of multiple valid star patterns. Ambiguity can also arise when no single star image contains enough stars to form an unambiguous pattern. False identification (aka “False Positives”) may arise when non-stellar objects (such as proton flashes, dust particles, satellites, asteroids, or comets) and stars form a pattern which lies within the measurement error radius of a valid star pattern. In these cases the algorithm may mistakenly believe it has correctly recognized a reference pattern. Misidentification (aka “False Negatives”) occurs when star measurement errors exceed the value assumed by the star pattern recognition algorithm, thereby causing a star pattern to be outside the measurement error radius assumed by the algorithm. In these cases the algorithm may mistakenly believe that no reference pattern is in the image. Complexity arises when the required number of features for pattern identification is increased (typically causing a polynomial or factorial increase in required memory or computation usage).
Ambiguity, false identification and misidentification can be reduced by reducing the measurement error radius of patterns through reduced star position measurement errors. Star position measurement errors can be reduced by combining data from multiple star images and then averaging the combined data. Ambiguity due to too few stars can be reduced by increasing the effective field of view to include more stars. Combining data from multiple offset star images can increase the effective field of view.
The foregoing examples of related art and limitations associated therewith are intended to be illustrative and not exclusive. Other limitations of the related art will become apparent to those of skill in the art upon a reading of the specification and a study of the drawings.